OFDM is a promising technique for high-data-rate wireless communications because it can combat inter-symbol interference (ISI) caused by the dispersive fading of wireless channels. The proposed research focuses on techniques that improve the performance of OFDM-based wireless communications and its commercial and military applications. In particular, we address the following aspects of OFDM: inter-channel interference (ICI) suppression, interference suppression for clustered OFDM, clustered OFDM based anti-jamming modulation, channel estimation for MIMO-OFDM, MIMO transmission with limited feedback. For inter-channel interference suppression, a frequency domain partial response coding (PRC) scheme is proposed to mitigate ICI. We derive the near-optimal weights for PRC that is independent on the channel power spectrum. The error floor resulting from ICI can be reduced significantly using a two-tap or a three-tap PRC. Clustered OFDM is a new technique that has many advantages over traditional OFDM. In clustered OFDM systems, adaptive antenna arrays are used for interference suppression. To calculate weights for interference suppression, we propose a polynomial-based parameter estimator to combat the severe leakage of the DFT based estimator due to the small size of the cluster. An adaptive algorithm is developed to obtain optimal performance. For high data rate military communications, we propose a clustered OFDM base spread spectrum modulation to provide both anti-jamming and fading suppression capability. We analyze the performance of uncoded and coded system. Employing multiple transmit and receive antennas in OFDM systems (MIMO-OFDM) can increase the spectral efficiency and link reliability. We develop a minimum mean-square-error (MMSE) channel estimator that takes advantage of the spatial-frequency correlations in MIMO-OFDM systems to minimize the estimation error. We investigate the training sequence design and two optimal training sequence designs are given for arbitrary spatial correlations. For a MIMO system, the diversity and array gains can be obtained by exploiting channel information at the transmitter. For MIMO-OFDM systems, we propose a subspace tracking based approach that can exploit the frequency correlations of the OFDM system to reduce the feedback rate. The proposed approach does not need recalculate the precoding matrix and is robust to multiple data stream transmission.

Yogurt is a characteristic fermented dairy product of Turkey and Bulgaria and its popularity has been increasing all over the world. Streptococcus thermophilus and Lactobacillus delbrueckii subsp. bulgaricus (Lactobacillus bulgaricus) are used together as starter culture in production of yogurt. The objective of this study was to isolate and characterize yogurt cultures from traditionally produced yogurts (i.e. produced without using commercial starter cultures) and to search the genotypic diversity within traditional S. thermophilus isolates. Yogurt cultures were isolated from traditionally produced yogurts collected from different regions of Turkey and identified biochemically. Acidification ability of the isolates was examined and the cultures giving best acidifying rates were further subjected to a selection in terms of their acetaldehyde production ability. Then, phage resistance and proteolytic activity of chosen isolates were tested. Finally, twenty-five L. bulgaricus and twenty-two S. thermophilus isolates were selected as cultures having best technological properties. Furthermore, subtyping studies were carried out to indicate strain diversity among isolates. S. thermophilus was selected as target organism for subtyping in this study. Clustered regularly interspaced short palindromic repeats (CRISPR) loci are highly polymorphic genetic regions, which are composed of partially palindromic direct repeats interspaced by sequences called spacers. In order to characterize S. thermophilus isolates genotypically, CRISPR1 locus of the isolates were analyzed. Additionally, nineteen isolates selected after CRISPR1 analysis were characterized using multilocus sequence typing (MLST). This provided to compare CRISPR1 analysis with MLST as a typing method. According to CRISPR1 analysis S. thermophilus isolates were grouped into 6 main clusters with a total of 15 sub-clusters. MLST results demonstrated an evolutionary relationship among these strains compatible with that derived from the CRISPR1 analysis.

No description available.

Family-centered care (FCC) focuses on considering the family in planning/implementing care and is associated with increased patient satisfaction. Little is known about factors that influence FCC. Using linear mixed modeling and Generalized Estimating Equations to analyze data from a cross-sectional survey of primary care practices in Ontario, this study sought to determine whether models of primary care service delivery differ in their provision of FCC and to identify characteristics of primary care practices associated with FCC. Patient-reported scores of FCC were high, but did not differ significantly among primary care models. After accounting for patient characteristics, practice characteristics were not significantly associated with patient-reported FCC. Provider-reported scores of FCC were significantly higher in Community Health Centres than in Family Health Networks. Higher numbers of nurse practitioners and clinical services on site were associated with higher FCC scores but scores decreased as the number of family physicians at a site increased.

Em diversos levantamentos científicos, nos deparamos com a dificuldade de coletar os dados devido ao objeto em estudo ser de difícil observação, como por exemplo em estudos com indivíduos portadores de doenças raras, ou dotados de um comportamento evasivo, ou ainda indivíduos que distribuem-se de maneira geograficamente esparsa. Neste trabalho estudamos esquemas de amostragem voltados para populações raras com especial atenção às populações raras e agrupadas. Nos aprofundamos nas técnicas de amostragem por conglomerados adaptativos e amostragem seqüencial em dois estágios, fornecendo ao leitor subsídio teórico para entender os fundamentos das técnicas, bem como compreender a eficácia de seus estimadores apresentada em estudos de simulações. Em nossos estudos de simulação, mostramos que a técnica de amostragem seqüencial em dois estágios não apresenta perdas de eficiência quando o agrupamento dos elementos é menor. Entretanto, os estudos comparativos revelam que quando a população é rara e agrupada, a eficiência para a amostragem por conglomerados adaptativos é maior na maioria das parametrizações utilizadas. Ao final deste trabalho, fornecemos recomendações para as situações a respeito do conhecimento da raridade e agrupamento da população em estudo. / In many surveys we find hard observing individuals, like in rare diseases, elusive individuals or sparsely distributed individuals. This work is about sampling schemes for rare populations, more specifically rare and clustered, driving our attention to adaptive cluster sampling and two stage sequential sampling giving readers their theoretical basis and simulated efficiencies evaluation. In our simulation studies, we found that the efficiency of two-stage sequential sampling does not decrease when sample clustering is low. However, the comparison studies show that when sample is rare and clustered, adaptive cluster sampling in the majority of tested cases has better efficiency. At the end of this study, there are recommendations for each situation of knowing rarity and clustering of the population in study.

Forward-looking financial disclosure (FLFD) is potentially uninformative if it does not change from the previous year, especially after a significant change in firm performance. This study uses a sample of UK narrative statements of the annual reports over the period from 2005 to 2011. It employed the automated content analysis technique to measure change in FLFD over years to answer three research questions. First, to what extent does change in firms’ earnings performance drive managers to change FLFD over years? Second, what are the other drivers of the change of FLFD from year to year? Third, do investors use information revealed by the change in FLFD? The study finds a positive association between change in FLFD and change in firm earnings performance. However, it finds weak evidence that firms with larger changes in their earnings performance are likely to change their FLFD more than those with smaller performance changes. In addition, when we distinguish between well-performing and poorly performing firms, it finds that the change in FLFD is more positively associated with poorly performing firms compared to well-performing firms. Furthermore, it finds that change in FLFD is positively (negatively) associated with firm size, (competitive environment), (litigious environment), and (percentage of managerial ownership). In addition, the role of the auditor in overseeing narrative reporting is not appearing for all sample firms or for well-performing firms, however, it is observable only in poorly performing firms. Finally, the study uses firm value three months after the release of the annual report to examine investors’ responses to the changes in FLFD. It finds that the value of a firm decreases as long as it changes its FLFD from the previous year. However, when we distinguish between well and poorly performing firms, it finds that the change in FLFD has no effect on the value of well-performing firms, while, it negatively affects poorly performing firms. The results suggest that FLFD in UK narratives includes some content about firm performance. However, it neither affects the value of well-performing firms nor enhances investors’ valuation of poorly performing firms.

Family-centered care (FCC) focuses on considering the family in planning/implementing care and is associated with increased patient satisfaction. Little is known about factors that influence FCC. Using linear mixed modeling and Generalized Estimating Equations to analyze data from a cross-sectional survey of primary care practices in Ontario, this study sought to determine whether models of primary care service delivery differ in their provision of FCC and to identify characteristics of primary care practices associated with FCC. Patient-reported scores of FCC were high, but did not differ significantly among primary care models. After accounting for patient characteristics, practice characteristics were not significantly associated with patient-reported FCC. Provider-reported scores of FCC were significantly higher in Community Health Centres than in Family Health Networks. Higher numbers of nurse practitioners and clinical services on site were associated with higher FCC scores but scores decreased as the number of family physicians at a site increased.

>Magister Scientiae - MSc / Saccharomyces cerevisiae is an important host in industrial biotechnology. This yeast is the host of choice for the first and second-generation biofuels for ethanol production. Genome modification in S. cerevisiae has been extremely successful largely due to this yeast’s highly efficient homology-directed DNA repair machinery. The advent of CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) genome editing technology has made multi-gene editing in yeast more accessible. In this study, we aimed at targeting the Cas9 to multiple genomic positions for integrating multiple genes at different sites. We have developed two CRISPR-Cas9 systems, based on published one- and two-plasmid systems, for application in S. cerevisiae strains. In this study, these CRISPR-Cas9 systems were used to transform fungal heterologous genes into yeast using the electroporation transformation method. We first utilized the CRISPR systems for targeting the T.r.eg2 gene to single locus chromosomal sites for single copy integration. Subsequently, we then targeted the same gene to repeated sequences in the genome, namely the delta sites, for multi-copy integration. The procedure was repeated with a different gene, T.e.cbh1, integrated into the same sites to ascertain reporter gene specific effects. High integration efficiency was achieved, since all the strains successfully integrated the genes. However, we discovered significant differences in enzyme activities between the two genes when targeted to different loci, as well as varying copy numbers as determined by qPCR. The T.e.cbh1 gene was highly expressed by yeast transformants targeted at the repeated delta sequences used for multi-copy integration, reaching maximum levels of 248 mU/gDCW. The T.r.eg2 gene was highly expressed in yeast transformants targeted to the single locus site on chromosome 12, reaching a maximum of 160U/gDCW, though it was shown that off-target integration likely occurred. We then used the information from these observations to construct a CBP yeast strain containing three cellulase genes: T.r.eg2, T.e.cbh1, and S.f.BGL1. Significant differences in enzyme activities were observed between the three genes, and it was shown that the S.f.BGL1 gene was poorly expressed by the CBP yeast strain, whereas the T.r.eg2 gene was highly expressed. Notably, due to the fact that marker containing plasmids could be cured from these strains, many additional genetic changes can still be made. Overall, our two CRISPR-Cas9 systems were efficient at engineering strains that produce recombinant proteins and can be used in future studies for a variety of applications, including metabolic engineering in S. cerevisiae

Commodity supercomputing clusters known as Beowulf clusters, have become a low cost alternative to traditional supercomputers. Beowulf clusters combine inexpensive computers and specialized software to achieve supercomputing power. The processing nodes in a diskless Beowulf cluster do not have a local hard disk unlike the nodes in most commodity clusters. Research has provided performance information for diskless clusters built with expensive, high performance equipment. Beowulf clusters use commodity off-the-shell hardware, and little information is available about their performance. This research includes the construction of several diskless Beowulf clusters. Using the NAS Parallel Benchmarks, the performance of these clusters was measured. Through analysis of these measurements, a descriptive performance model of diskless Beowulf clusters was produced.

Pinpointing the source of a software failure based on log files can be a time consuming process. Automated log analysis tools are meant to streamline such processes, and can be used for tasks like failure diagnosis. This thesis evaluates three supervised models for failure diagnosis of clustered log data. The goal of the thesis is to compare the performance of the models on industry data, as a way to investigate whether the chosen ML techniques are suitable in the context of automated log analysis. A Random Forest, an SVM and an MLP are generated from a dataset of 194 failed executions of tests on microservices, that each resulted in a large collection of logs. The models are tuned with random search and compared in terms of precision, recall, F1-score, hold-out accuracy and 5-fold cross-validation accuracy. The hold-out accuracy is calculated as a mean from 50 hold-out data splits, and the cross-validation accuracy is computed separately from a single set of folds. The results show that the Random Forest scores highest in terms of mean hold-out accuracy (90%), compared to the SVM (86%) and the Neural Network (85%). The mean cross-validation accuracy is the highest for the SVM (95%), closely followed by the Random Forest (94%), and lastly the Neural Network (85%). The precision, recall and F1-score are stable and consistent with the hold-out results, although the precision results are slightly higher than the other two measures. According to this evaluation, the Random Forest has the overall highest performance on the dataset when considering the hold-out- and cross-validation accuracies, and also the fact that it has the lowest complexity and thus the shortest training time, compared to the other considered solutions. All in all, the results of the thesis demonstrate that supervised learning is a promising approach to automatize log analysis. / Att identifiera orsaken till en misslyckad mjukvaruexekvering utifrån logg-filer kan vara en tidskrävande process. Verktyg för automatiserad logg-analysis är tänkta att effektivisera sådana processer, och kan bland annat användas för feldiagnos. Denna avhandling tillhandahåller tre övervakade modeller för feldiagnos av klustrad logg-data. Målet med avhandlingen är att jämföra modellernas prestanda på data från näringslivet, i syfte att utforska huruvida de valda maskininlärningsteknikerna är lämpliga för automatiserad logg-analys. En Random Forest, en SVM och en MLP genereras utifrån ett dataset bestående av 194 misslyckade exekveringar av tester på mikrotjänster, där varje exekvering resulterade i en stor uppsättning loggar. Modellerna finjusteras med hjälp av slumpmässig sökning och jämförs via precision, träffsäkerhet, F-poäng, noggrannhet och 5-faldig korsvalidering. Noggrannheten beräknas som medelvärdet av 50 datauppdelningar, och korsvalideringen tas fram separat från en enstaka uppsättning vikningar. Resultaten visar att Random Forest har högst medelvärde i noggrannhet (90%), jämfört med SVM (86%) och Neurala Nätverket (85%). Medelvärdet i korsvalidering är högst för SVM (95%), tätt följt av Random Forest (94%), och till sist, Neurala Nätverket (85%). Precisionen, träffsäkerheten och F-poängen är stabila och i enlighet med noggrannheten, även om precisionen är något högre än de andra två måtten. Enligt den här analysen har Random Forest överlag högst prestanda på datasetet, med hänsyn till noggrannheten och korsvalideringen, samt faktumet att denna modell har lägst komplexitet och därmed kortast träningstid, jämfört med de andra undersökta lösningarna. Sammantaget visar resultaten från denna avhandling att övervakad inlärning är ett lovande tillvägagångssätt för att automatisera logg-analys.